- Find A is causal, rather than random, consequence.
- How do we identify the causes of A?
- How do we justify B is a cause of A?
How to understand causation?
Pluralist view?
- \(\neq\) multicausality
Pluralist view: formal causes, material causes, efficient causes, deterministic causes, probabilistic causes, correlational causation, causal mechanisms…
- Different explanations for the same causation
- Different perspective of the same causal explanation.
Why not pluralist view?
- Over-stating the different-ness
- Not benefiting the research
A universal view
- A minimal definition
- 16 criteria of formal properties of causal arguments
- 8 criteria for research design
Defining causation
Cause: Events or conditions that raise the prior probability of some outcome occurring, under ceteris paribus conditions (Gerring 2005, 169).
- \(P(Y|X) > P(Y|-X).\)
- Why a minimal causation?
- Hint: Sartori's ladder
- Bayesian framework?
- \(Y(A|B) = \frac{Y(B|A)Y(A)}{Y(B)}\)
- \(Posterior = \frac{Likelihood\times Prior}{Evidence}\)
Causal Proposition
- Specification
- Precision
- Breadth
- Boundedness
- Completeness
- Parsimony
- Differentiation (exogeneity)
- Priority
- Independence
- Contingency
- Mechanism
- Analytic utility
- Intelligibility
- Relevance
- Innovation
- Comparison
Criteria of Demonstration
- Plenitude
- Comparability
- Independence
- Representativeness
- Variation
- Transparency
- Replicability
Plenitude
Conducting an empirical based study.
Nope
Yes
Comparability
- Descriptive comparability: 'X' and 'Y' mean roughly the same thing across cases.
- Causal comparability: X and Y do not interact in idiosyncratic ways in different cases.
- Control: the extent to which remaining dissimilarities (of both sorts) may be taken into account.
Independence and Representativeness
Variation
Transparency
Replicability
Two strategies to test theory
- Actual case strategy (save for later)
- Counterfactual strategy
What's counterfactual?
- Claims about events that did not actually occur.
Relation with hypothesis test?
- Following the experimental logic
- Compromising with the reality
Differences from the hypothesis test
Hypothesis test
- Rely on "ceteris paribus"
- Some probability assumptions
- Can assess the frequencies and magnitudes of the causality
- Uncertainty can be reduced by more cases
Counterfactual
- Rely on general principles, laws, or regularities
- Knowledge of historical facts
- Assess effects based on proliferation
- No formal criterion of uncertainty
Why not actual cases?
- Comparability
- Degree of freedom
When to use?
- Qualitative, mostly
- # of variables > # of observations
Risk
How can we know what would have happened?
